[1] Ding Youliang, Li Aiqun, Geng Fangfang,. Damage warning of suspension bridgesbased on neural networks under changing temperature conditions [J]. Journal of Southeast University (English Edition), 2010, 26 (4): 586-590. [doi:10.3969/j.issn.1003-7985.2010.04.018]

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Damage warning of suspension bridgesbased on neural networks under changing temperature conditions()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

26

Issue:

2010 4

Page:

586-590

Research Field:

Civil Engineering

Publishing date:

2010-12-30

Info

Title:

Damage warning of suspension bridgesbased on neural networks under changing temperature conditions

Author(s):

Ding Youliang¹;  Li Aiqun¹;  Geng Fangfang²

¹ Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
² Chengxian College, Southeast University, Nanjing 210096, China

Keywords:

structural damage detection;  modal frequency;  temperature;  neural network;  suspension bridge

PACS:

TU311.41

DOI:

10.3969/j.issn.1003-7985.2010.04.018

Abstract:

This paper aims at successive structural damage detection of long-span bridges under changing temperature conditions. First, the frequency-temperature correlation models of bridges are formulated by means of artificial neural network techniques to eliminate the temperature effects on the measured modal frequencies. Then, the measured modal frequencies under various temperatures are normalized to a reference temperature, based on which the auto-associative network is trained to monitor signal damage occurrences by means of neural-network-based novelty detection techniques. The effectiveness of the proposed approach is examined in the Runyang Suspension Bridge using 236-day health monitoring data. The results reveal that the seasonal change of environmental temperature accounts for variations in the measured modal frequencies with averaged variances of 2.0%. And the approach exhibits good capability for detecting the damage-induced 0.1% variance of modal frequencies and it is suitable for online condition monitoring of suspension bridges.

References:

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[8] Hua X G, Ni Y Q, Ko J M, et al. Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique [J]. Journal of Computing in Civil Engineering, 2007, 21(2): 122-135.
[9] Ding Y L, Li A Q. Structural health monitoring of long-span suspension bridges using wavelet packet analysis [J]. Earthquake Engineering and Engineering Vibration, 2007, 6(3): 289-294.
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[11] Ko J M, Sun Z G, Ni Y Q. Multi-stage identification scheme for detecting damage in cable-stayed Kap Shui Mun Bridge [J]. Engineering Structures, 2002, 24(7): 857-868.

Memo

Memo:

Biographies: Ding Youliang(1979—), male, doctor, associate professor, civilding@163.com; Li Aiqun(1962—), male, doctor, professor, aiqunli@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.50725828, 50808041), the Natural Science Foundation of Jiangsu Province(No.BK2008312), the Ph.D. Programs Foundation of Ministry of Education of China(No.200802861011).
Citation: Ding Youliang, Li Aiqun, Geng Fangfang. Damage warning of suspension bridges based on neural networks under changing temperature conditions[J].Journal of Southeast University(English Edition), 2010, 26(4):586-590.

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